A deep unsupervised clustering-based post-processing framework for high-fidelity Cerenkov luminescence tomography

被引:6
|
作者
Cao, Xin [1 ]
Zhang, Jun [1 ]
Yang, Jianan [1 ]
Fan, Chunxiao [2 ]
Zhao, Fengjun [1 ]
Zhou, Wei [1 ]
Wang, Lin [1 ]
Geng, Guohua [1 ]
Zhou, Mingquan [3 ]
Chen, Xueli [4 ,5 ]
机构
[1] Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Shaanxi, Peoples R China
[2] Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230601, Anhui, Peoples R China
[3] Beijing Normal Univ, Engn Res Ctr Virtual Real & Applicat, Beijing Key Lab Digital Preservat & Virtual Real, Minist Educ, Beijing, Peoples R China
[4] Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Xian 710071, Shaanxi, Peoples R China
[5] Xidian Univ, Sch Life Sci & Technol, Xian 710071, Shaanxi, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
SPARSE RECONSTRUCTION; POWER; MODEL;
D O I
10.1063/5.0025877
中图分类号
O59 [应用物理学];
学科分类号
摘要
Cerenkov Luminescence Tomography (CLT) is a promising optical molecular imaging technology. It involves the three-dimensional reconstruction of the distribution of radionuclide probes inside a single object to indicate a tumor's localization and distribution. However, reconstruction using CLT suffers from severe ill-posedness, resulting in numerous artifacts within the reconstructed images. These artifacts influence the visual effect and may misguide the medical professional (diagnostician), resulting in a wrong diagnosis. Here, we proposed a deep unsupervised clustering-based post-processing framework to eliminate artifacts and facilitate high-fidelity CLT. First, an initial reconstructed image was obtained by a specific reconstruction method. Second, voxel data were generated based on the initial reconstructed result. Third, these voxels were divided into three groups, and only the group with the highest mean intensity was chosen as the final reconstructed result. A group of numerical simulation and in vivo mouse-based experiments were conducted to assess the presented framework's feasibility and potential. The results indicated that the proposed framework could reduce the number of artifacts effectively. The reconstructed image's shape and distribution were more similar to the actual light source than those obtained without the proposed framework.
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页数:11
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